{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Channel  Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "0        2       3  12669  9656     7561     214              2674        1338\n",
       "1        2       3   7057  9810     9568    1762              3293        1776\n",
       "2        2       3   6353  8808     7684    2405              3516        7844\n",
       "3        1       3  13265  1196     4221    6404               507        1788\n",
       "4        2       3  22615  5410     7198    3915              1777        5185"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "customer = pd.read_csv('E:\\\\Wholesale_customers_data.csv')   #文件名不能有空格\n",
    "customer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 440 entries, 0 to 439\n",
      "Data columns (total 8 columns):\n",
      " #   Column            Non-Null Count  Dtype\n",
      "---  ------            --------------  -----\n",
      " 0   Channel           440 non-null    int64\n",
      " 1   Region            440 non-null    int64\n",
      " 2   Fresh             440 non-null    int64\n",
      " 3   Milk              440 non-null    int64\n",
      " 4   Grocery           440 non-null    int64\n",
      " 5   Frozen            440 non-null    int64\n",
      " 6   Detergents_Paper  440 non-null    int64\n",
      " 7   Delicassen        440 non-null    int64\n",
      "dtypes: int64(8)\n",
      "memory usage: 27.6 KB\n"
     ]
    }
   ],
   "source": [
    "#查看数据缺失值---数据并没有缺失值，数据完整性比较好\n",
    "customer.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.322727</td>\n",
       "      <td>2.543182</td>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.468052</td>\n",
       "      <td>0.774272</td>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Channel      Region          Fresh          Milk       Grocery  \\\n",
       "count  440.000000  440.000000     440.000000    440.000000    440.000000   \n",
       "mean     1.322727    2.543182   12000.297727   5796.265909   7951.277273   \n",
       "std      0.468052    0.774272   12647.328865   7380.377175   9503.162829   \n",
       "min      1.000000    1.000000       3.000000     55.000000      3.000000   \n",
       "25%      1.000000    2.000000    3127.750000   1533.000000   2153.000000   \n",
       "50%      1.000000    3.000000    8504.000000   3627.000000   4755.500000   \n",
       "75%      2.000000    3.000000   16933.750000   7190.250000  10655.750000   \n",
       "max      2.000000    3.000000  112151.000000  73498.000000  92780.000000   \n",
       "\n",
       "             Frozen  Detergents_Paper    Delicassen  \n",
       "count    440.000000        440.000000    440.000000  \n",
       "mean    3071.931818       2881.493182   1524.870455  \n",
       "std     4854.673333       4767.854448   2820.105937  \n",
       "min       25.000000          3.000000      3.000000  \n",
       "25%      742.250000        256.750000    408.250000  \n",
       "50%     1526.000000        816.500000    965.500000  \n",
       "75%     3554.250000       3922.000000   1820.250000  \n",
       "max    60869.000000      40827.000000  47943.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fresh列到Delicassen列的数据数量级大小差别很大，在聚类前需要进行标准化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.70833271, 0.53987376, 0.42274083, 0.01196489, 0.14950522,\n",
       "        0.07480852],\n",
       "       [0.44219826, 0.61470384, 0.59953989, 0.11040858, 0.20634248,\n",
       "        0.11128583],\n",
       "       [0.39655169, 0.5497918 , 0.47963217, 0.15011913, 0.2194673 ,\n",
       "        0.48961931],\n",
       "       ...,\n",
       "       [0.36446153, 0.38846468, 0.7585445 , 0.01096068, 0.37223685,\n",
       "        0.04682745],\n",
       "       [0.93773743, 0.1805304 , 0.20340427, 0.09459392, 0.01531   ,\n",
       "        0.19365326],\n",
       "       [0.67229603, 0.40960124, 0.60547651, 0.01567967, 0.11506466,\n",
       "        0.01254374]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import Normalizer\n",
    "train_data = customer.iloc[:,2:]\n",
    "norm_train_data = Normalizer().fit_transform(train_data)\n",
    "norm_train_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 适用轮廓系数找到最优K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'score')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans \n",
    "from sklearn.metrics import silhouette_score #轮廓系数\n",
    "clusters = [2,3,4,5,6]\n",
    "score=[]\n",
    "for k in clusters:\n",
    "    kM  = KMeans(n_clusters=k)\n",
    "    kM.fit(norm_train_data)\n",
    "    score.append(silhouette_score(norm_train_data,kM.labels_))\n",
    "sns.lineplot(clusters,score)\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 适用SSE找到最优K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'SSE')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SSE = [] #误差平方和\n",
    "for i in clusters:\n",
    "    kmeans = KMeans(n_clusters=i)\n",
    "    kmeans.fit(norm_train_data)\n",
    "    SSE.append(kmeans.inertia_)\n",
    "sns.lineplot(clusters,SSE)\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('SSE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建模及可视化分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.907702</td>\n",
       "      <td>0.171099</td>\n",
       "      <td>0.226181</td>\n",
       "      <td>0.150525</td>\n",
       "      <td>0.049222</td>\n",
       "      <td>0.071186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.262553</td>\n",
       "      <td>0.488689</td>\n",
       "      <td>0.677848</td>\n",
       "      <td>0.095956</td>\n",
       "      <td>0.266153</td>\n",
       "      <td>0.100561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.575088</td>\n",
       "      <td>0.233245</td>\n",
       "      <td>0.279252</td>\n",
       "      <td>0.625425</td>\n",
       "      <td>0.053748</td>\n",
       "      <td>0.109052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicassen\n",
       "0  0.907702  0.171099  0.226181  0.150525          0.049222    0.071186\n",
       "1  0.262553  0.488689  0.677848  0.095956          0.266153    0.100561\n",
       "2  0.575088  0.233245  0.279252  0.625425          0.053748    0.109052"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#建模分类（3类）\n",
    "kM = KMeans(n_clusters=3,init='k-means++',random_state=10)\n",
    "kM.fit(norm_train_data)\n",
    "center = pd.DataFrame(kM.cluster_centers_,columns=['Fresh','Milk','Grocery','Frozen','Detergents_Paper','Delicassen'])\n",
    "center"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x25c7505deb0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "km_labels = pd.Series(kM.labels_)\n",
    "sizes = km_labels.value_counts(1).values\n",
    "labels = km_labels.value_counts(1).index\n",
    "plt.axis('equal')\n",
    "plt.pie(sizes,autopct='%.f%%')\n",
    "plt.legend(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x25c751577c0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "center.plot(kind='bar')\n",
    "plt.xticks(rotation=0)\n",
    "plt.legend(bbox_to_anchor=(1.01, 0.56), loc=3, borderaxespad=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 疑问\n",
    "如下所示,Fresh列到Delicassen列的数据都有离群点,是否应先去除离群点,去除离群点的标准怎么确定?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 6, figsize=(24, 4))\n",
    "for i in range(2, 8):\n",
    "    sns.boxplot(y=customer[customer.columns[i]], data=customer, ax=ax[i-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
